Auto manufacturers are investigating radar, lidar, and vision-based pre-crash sensing systems to improve occupant safety. Current vehicles typically employ accelerometers that measure decelerations acting on the vehicle body in the event of a crash. In response to accelerometers, airbags or other safety devices are activated.
In certain crash situations it would be desirable to provide information before forces actually act upon the vehicle when a collision is unavoidable.
Remote sensing systems using radar, lidar or vision based technologies for adaptive cruise control, collision avoidance and collision warning applications are known. These systems have characteristic requirements for false alarms. Generally, the remote sensing system reliability requirements for pre-crash sensing for automotive safety related systems are more stringent than those for comfort and convenience features, such as, adaptive cruise control. The reliability requirements even for safety related features vary significantly, depending upon the safety countermeasure under consideration. For example, tolerance towards undesirable activations may be higher for activating motorized belt pre-tensioners than for functions such as vehicle suspension height adjustments. Non-reversible safety countermeasures, including airbags, require extremely reliable sensing systems for pre-crash activation. Innovative algorithms are required to verify the information received from sensor systems and to predict the potential for impact within the limitations of the sensing system, while maintaining reliability and minimization of false alarms. It would be desirable to have coordinated decision-making on when to activate safety systems such as active front suspension systems and motorized belt pre-tensioners. In addition, strategies for reversing activation, minimization of predictive sensor error, while accounting for occupant characteristics, are desired.
It would therefore be desirable to provide a pre-crash sensing system that accurately determines the potential threat of an object, by capitalizing on the performance of multiple sensor systems. There is also a need in the art to provide coordinated decision-making for countermeasure activation based on an aggregate evaluation of sensor and countermeasure dynamics to accomplish these desires.